Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.
In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics.
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting.
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
Ranked #1 on Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)
Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks.
However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.